Social networks have revolutionized electronic communications by providing users with interactive ways to communicate and connect with one another. Users of social networks can, for instance, exchange electronic messages and create relationships with each other or with particular communities in the social network. Some social networks also provide public and private message boards for users to express ideas and share images, video, and interactive content. Due to the popularity of these and other features, a significant portion of humanity maintains membership with some form of social network.
Routine usage of a social network can result in the generation of large volumes of data relating to that usage, including log data regarding user activity on a social network or relating to various systems that provide or support the social network. Data generated by social networks can include data relating to how various segments (e.g., features) of a social network are performing, data relating to how various systems that support the social network are performing, data relating to trends in content generated or accessed by users, behavioral data regarding users, and the like. These and other types of data can be useful in performing analytics on the social network.
Due to its storage footprint and rate of generation, managing and querying data generated by a social network can prove to be a difficult task for operators of the underlying system. For example, quality assurance teams and product development teams may find it difficult to analyze data relating to usage of newly deployed or longstanding social network features. Likewise, as another example, those teams may encounter difficulties in assessing the performance of various system components that support those features.